Wheel aligner with advanced diagnostics and no-stop positioning

ABSTRACT

A vehicle wheel alignment system has a plurality of cameras, each camera for viewing a respective target disposed at a respective wheel of the vehicle and capturing image data of the target as the wheel and target are continuously rotated a number of degrees of rotation without a pause. The image data is used to calculate a minimum number of poses of the target of at least one pose for every five degrees of rotation as the wheel and target are continuously rotated the number of degrees of rotation without a pause. At least one of the cameras comprises a data processor for performing the steps of preprocessing the image data, and calculating an alignment parameter for the vehicle based on the preprocessed image data.

RELATED APPLICATIONS

The present application claims priority to U.S. Provisional ApplicationNo. 62/238,017, entitled “Portable Vehicle Aligner With High SpeedCamera Based Processing and Advanced Analytics,” filed Oct. 6, 2015,which is incorporated herein by reference in its entirety.

BACKGROUND OF THE INVENTION Field

This invention relates to an automotive wheel aligner of the machinevision type.

Background

Conventional camera-based 3D machine vision-type aligners are not veryportable. Camera based wheel aligners have a pair of cameras mountedsuch that each camera can look down a side of the vehicle at targetsattached to the wheels. The cameras are usually mounted about 100″ apartand are about 100″ in front of the vehicle. Also, the vehicle needs tobe raised so the alignment technician can work underneath it. Thus, thealigner cameras either have to move up and down with the rack, or bemounted high enough to be able to see the targets when the rack israised. The cameras can be over 100″ off the ground when raised.Conventional aligners also include a console that may house a monitor,printer, computer, and target and clamp storage. Many camera basedaligners' cameras are bolted to the ground and cannot be moved from onealignment bay to the next. However, it is useful to be able to move thealigner to another bay, because the vehicle under test might requireparts, and removing it from the rack is not feasible. In such a case,the aligner is no longer useable. It is therefore highly desirable to beable to move it to a new location.

There are some camera based aligners that are movable; however, thesehave some significant limitations. In one of these aligners, the camerabeam is simply bolted to the side or back of the console. This makes fora very large assembly that needs to be rolled together through the shop.This assembly is over a 100″ wide, 36″ deep and at least 80″ high. Thereis no easy way to maneuver this aligner in what typically is a crowdedshop environment. Another portable camera based aligner has separatepoles for the cameras and the console, so the technician has to movethree discrete components to the new location. Also, the cameras aremounted on poles that are over 100″ tall, and they are difficult to movethrough doors or other areas with a low ceiling. There are also othertypes of camera aligners that mount to the rack. These are smaller sincethere is no pole; however, there are special mounting requirements foreach rack. It is clear that a better solution to the portable camerabased aligner is desirable.

Machine vision vehicle alignment systems using movable cameras andtargets attached to vehicle wheels, also known as “image aligners,” arewell known. The targets are viewed by the cameras such that image dataobtained for a prescribed alignment process can be used to calculatevehicle alignment angles for display through a user interface, usually acomputer monitor. Early system implementations included rigid beams thatconnected the cameras so that their position and orientation withrespect to each other could be determined and be relied upon asunchanging. Later system implementations were introduced comprising theuse of cameras not rigidly connected to each other, but using a separatecamera/target system to continuously calibrate the position of onevehicle mounted target viewing camera to another. This type of system isdescribed in U.S. Pat. Nos. 5,535,522; 6,931,340; 6,959,253; and6,968,282, all of which are hereby incorporated by reference herein intheir entirety. An example of a vehicle wheel aligner using such imageprocessing is the Visualiner 3D or “V3D”, commercially available fromJohn Bean Company, Conway, Ark., a division of Snap-on Incorporated

There are many factors that influence the measurement of a vehicle thatcan lead to a good aligner and a good mechanic to get bad readings. Anuneven roll surface during the roll back procedure can cause the wheelto move or rotate about a different axis than the one it is rollingabout. Camera based aligners require measurements of alignmentparameters, which include an axis of rotation, a wheel angle, a vehiclewheel plane, or a vehicle wheel center. Another influence in badreadings is if, during caster swing, the vehicle brakes are not lockedand the vehicle rolls, or the skid plates and turntables are notunlocked or are sticky, then large forces can build up within thevehicle and cause a bad reading. Also, if during adjustment the skidplates or turn tables are sticky, there is a buildup of forces withinthe vehicle that adversely affect the alignment. Other areas ofcontribution to reduced accuracy is if the rack is twisted, or thevehicle is jacked up and not let down and settled correctly.

There is a need for an aligner to incorporate cameras that can takemeasurements fast enough and continuously to measure, detect, andcorrect or warn of a problem with the alignment measurement. Further,there is a need for an aligner that measures, detects or corrects allpossible issues that might make the alignment readings incorrect. Withsuch an aligner the technician can be confident that the alignment justperformed is correct.

Current camera based aligners require a positioning or runout procedureto measure the wheel axis (also referred to as “wheel spindle” or “axisof rotation” throughout this document), and a steering swing procedureto measure the caster and steering axis inclination (SAI). Thisprocedure is time consuming and frequently requires the technician tohold the vehicle steady at certain points of the positioning and casterswing.

There is a need for a faster positioning procedure and a faster casterswing procedure, where the mechanic can perform the procedure withoutstops or delays.

When an aligner is in need of repair, a service technician is sent tothe site. Sometimes the issue could be solved if the service techniciancould operate the aligner remotely, avoiding a costly service call.Methods exist to take control of an aligner using special software andan internet connection, but there are limits to their capabilities.

There is a need for a remote display/interface for the technician onsite using the equipment, and a remote display/interface that servicepersonnel at a different location can use to control the aligner, all ona simple readily available device. A solution that easily does both ofthese in one simple architecture would be highly desirable.

BRIEF SUMMARY

Given the foregoing, the ideal camera based aligner is self-contained ona single console with a height and width to navigate through the typicalshop. Shops are laid out so mechanics have the ability to maneuvereasily from area to area, and thus it is desirable that theself-contained aligner fits through these areas. Accordingly, thedisclosed aligner incorporates the following features in severalembodiments:

-   -   Self-calibrating cameras attached to arms that fold, to reduce        the width of the aligner.    -   Folding arms movably attached to the console to minimize the        width of the console, resulting in a single self-contained        aligner and console.    -   A locking mechanism to hold the arms closed, yet have easy        unlocking for regular width alignment.    -   A screw shaft and mechanism to lower and lock the arms, and to        lower the center of gravity and overall height of the aligner to        facilitate less tipping and pass-way through low ceilings or        doorways. Vertical movement with the screw shaft is reduced or        eliminated.    -   Various methods of automatically closing the arms when they are        lowered for easier storage and transportation. For example, two        posts on the side of the console that push up the arms, an        internal extra slide within the slide car that pulls the arms        closed, another mechanical motor and mechanism to pull the arms        closed.    -   Wheel clamps/targets mounted on the console at an angle to        minimize the depth and width of the console.    -   Clamshell “structural” arms to reduce the weight and cost of the        arms, thereby allowing for a small torque motor and lower power        requirement, thus further reducing weight and costs.

Self-contained portable aligners can advantageously be moved from onelocation or alignment bay to the next with ease. However, they typicallylack functionality once they are located at the alignment bay. When acar is being aligned it is desirable to be able to see the readings fromone side or the other side of the vehicle. Self-contained portablealigners have all the equipment for measurement in a single easy to movedesign, but if desired it is difficult to move just the display deviceto the side of the vehicle so the technician can see it. Being all inone, if the technician moves the console for easy viewing of thedisplay, he has also moved the cameras and they can no longer see thewheel targets. There is a need for a portable aligner that once in thebay could also be portable to be able to view the display device. Thedisclosed aligner addresses the foregoing problems by incorporating thefollowing features in several embodiments:

-   -   A nesting console with a portable camera base.    -   Wireless communication between the cameras and the console.

The ideal camera based aligner also has a rapid camera measurementdevice to measure the discrete elements that make up certain criticalalignment procedures, in order to measure, detect, correct, or warn ofproblems. It also has the ability to perform procedures to measure thevehicle wheel axis and the caster and steering axis inclination (SAI)continuously, without pausing or holding. Accordingly, the disclosedaligner incorporates the following features in several embodiments:

-   -   A rapid camera measurement device to measure the discrete        elements that make up the positioning procedure to measure,        detect, correct, or warn of issues.    -   A rapid camera measurement device to measure the discrete        elements that make up the caster swing procedure to measure,        detect, correct, or warn of issues.    -   A rapid camera measurement device to measure the discrete        elements that make up the adjustment of the vehicle to measure,        detect, correct, or warn of issues.    -   A rapid camera measurement device to measure the discrete        elements that make up the jacking and settling of the vehicle to        measure, detect, correct, or warn of issues.    -   Thrust Angle checking before and after procedures that would        indicate the alignment rack's turntables or skid plates are        sticking.    -   Rack twist measurement correct or warn.    -   Detection of suspension changes during alignment or measurement        procedures to correct and/or warn.    -   Continual rapid rigid body analysis of measurements to detect        stress build ups, and warn or correct.    -   Non-stop runout procedures and caster swing procedures.

The disclosed aligners also include a fully networked system aligner(web server) for complete, simple, inexpensive functionality of remotedisplay and control of the aligner from the user's shop or from theservice desk many miles away.

In an embodiment of the present disclosure, a vehicle wheel alignmentsystem comprises a plurality of cameras, each camera for viewing arespective target disposed at a respective wheel of the vehicle andcapturing image data of the target as the wheel and target arecontinuously rotated a number of degrees of rotation without a pause.The image data is used to calculate a minimum number of poses of thetarget of at least one pose for every five degrees of rotation capturedby each camera as the wheel and target are continuously rotated thenumber of degrees of rotation without a pause. At least one of thecameras comprises a data processor for performing the steps ofpreprocessing the image data, and calculating an alignment parameter forthe vehicle based on the preprocessed image data.

Further features and advantages of the present disclosure, as well asthe structure and operation of various embodiments of the presentdisclosure, are described in detail below with reference to theaccompanying drawings. It is noted that the present invention is notlimited to the specific embodiments described herein. Such embodimentsare presented herein for illustrative purposes only. Additionalembodiments will be apparent to persons skilled in the relevant art(s)based on the teachings contained herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated herein and form partof the specification, illustrate the present invention and, togetherwith the description, further serve to explain the principles of thepresent invention and to enable a person skilled in the relevant art(s)to make and use the present invention.

Additionally, the left-most digit of a reference number identifies thedrawing in which the reference number first appears (e.g., a referencenumber ‘310’ indicates that the element so numbered is first labeled orfirst appears in FIG. 3). Additionally, elements which have the samereference number, followed by a different letter of the alphabet orother distinctive marking (e.g., an apostrophe), indicate elements whichare the same in structure, operation, or form but may be identified asbeing in different locations in space or recurring at different pointsin time (e.g., reference numbers ‘110 a’ and ‘110 b’ may indicate twodifferent energy detection devices which are functionally the same, butare located at different points in a simulation arena).

FIG. 1 is a flow chart illustrating a data collection procedure for thecompensation roll according to an embodiment of the present disclosure.

FIG. 2 is a flow chart illustrating a plurality of diagnostic checksaccording to an embodiment of the present disclosure.

FIG. 3 is a flow chart illustrating a surface flatness checkingprocedure according to an embodiment of the present disclosure.

FIG. 4 is a flow chart illustrating a plate slipping checking procedureaccording to an embodiment of the present disclosure.

FIG. 5 is a graph depicting wheel translation vs. rotation fordetermining plate slipping.

FIG. 6 is a graph depicting fit error vs. rotation for determining plateslipping.

FIG. 7 is a graph depicting axes of rotation vs. axis of precession fora wheel having wobble.

FIGS. 8 and 9 are flow charts illustrating a wheel wobble checkingprocedure according to an embodiment of the present disclosure.

FIG. 10 is a flow chart illustrating a camera instability checkingprocedure according to an embodiment of the present disclosure.

FIG. 11 is a flow chart illustrating a suspension stress checkingprocedure according to an embodiment of the present disclosure.

FIGS. 12 and 13 are flow charts illustrating a thrust angle checkingprocedure according to an embodiment of the present disclosure.

FIG. 14 is a flow chart illustrating a non-static runout compensationprocedure according to an embodiment of the present disclosure.

FIG. 15 is a flow chart illustrating a non-static caster swing procedureaccording to an embodiment of the present disclosure.

FIG. 16 is a schematic top plan view of a self-calibrating wheel alignerwith which the disclosed system and methodology can be implemented.

FIG. 17 is a schematic top plan view of a hybrid wheel aligner withwhich the disclosed system and methodology can be implemented.

FIGS. 18-22 are perspective views of a portable self-calibrating wheelaligner according to embodiments of the present disclosure.

FIGS. 23a-c are views of a self-calibrating portable wheel alignerhaving a mobile console according to an embodiment of the presentdisclosure.

FIGS. 24a-c are views of a self-calibrating portable wheel alignerhaving a movable display according to an embodiment of the presentdisclosure.

FIGS. 25a-b are views of a self-calibrating portable wheel alignerhaving a movable display according to another embodiment of the presentdisclosure.

FIG. 26 is a block diagram of a networked wheel aligner according to anembodiment of the present disclosure.

Further embodiments, features, and advantages of the present invention,as well as the operation of the various embodiments of the presentinvention, are described below with reference to the accompanyingfigures.

DETAILED DESCRIPTION OF THE INVENTION

While embodiments described herein are illustrative embodiments forparticular applications, it should be understood that the invention isnot limited thereto. Those skilled in the art with access to theteachings provided herein will recognize additional modifications,applications, and embodiments within the scope thereof and additionalfields in which the invention would be of significant utility.

The embodiments described herein are referred in the specification as“one embodiment,” “an embodiment,” “an example embodiment,” etc. Thesereferences indicate that the embodiment(s) described can include aparticular feature, structure, or characteristic, but every embodimentdoes not necessarily include every described feature, structure, orcharacteristic. Further, when a particular feature, structure, orcharacteristic is described in connection with an embodiment, it isunderstood that it is within the knowledge of one skilled in the art toaffect such feature, structure, or characteristic in connection withother embodiments whether or not explicitly described.

Self-Calibrating Aligner with Improved Portability

An aligner with certain discrete components that can be fullydisassembled and assembled in any configuration is shown in FIGS. 18-20.Referring to FIG. 18, an embodiment of the disclosed aligner includescamera pods 16 g, 16 h, a base 16 m, an upright column 16 f attached tobase 16 m via base plate 16 e, target and clamp storage 16 c fortarget/clamp assemblies 16 d, a printer 16 j, a keyboard shelf 16 a, anda display device which can include a monitor 16 b. The term “camera pod”as used herein refers to a housing for at least one camera. In someembodiments, the camera pods also house other additional equipment suchas a calibration target, a wireless communications module, etc. asdiscussed in detail herein below. The display device in certainembodiments can include a host personal computer (PC) and a monitor, asmart TV, a laptop computer, a tablet such as an Apple iPad™, aHewlett-Packard Chromebook™, or any other device capable of running abrowser.

All of the components can be assembled on common base 16 m and be anall-in-one portable aligner (FIG. 18). The monitor 16 b of the displaydevice can be mounted to the upright column 16 f or to the keyboardshelf 16 a. The camera pods 16 g, 16 h, upright column 16 f, and baseplate 16 e comprise a self-contained unit that can be bolted to themobile base 16 m, to the floor, or to a suitable wall. The clamp hangerbrackets 16 c can be attached to the keyboard shelf 16 a or to anotherlocalized structure such as a wall 18 c, alignment lift, or freestandingpost 18 a, 18 b (FIG. 20). The printer 16 j could be installed in theconsole 16 a or in another location more convenient for the end user. Ina further embodiment shown in FIGS. 23a-c , a second mobile stand 2300is used to support the monitor 16 b of the display device and/or userinput devices and/or printer 16 j.

The disclosed 3D visual aligner includes a three-camera self-calibratingmeasuring system having a first camera pod 16 g and a second camera pod16 h. First camera pod 16 g comprises a forward-facing first camera 16 pfor viewing a pair of targets (such as the targets T of clamps 16 d)when the targets T are attached to the left-side wheels of the vehicleto be aligned, and a calibration camera 16 s extending from the rear ofthe pod 16 g and facing the second camera pod 16 h. Second camera pod 16h comprises a forward-facing second camera 16 r for viewing a pair oftargets (such as the targets T of clamps 16 d) when the targets areattached to the right-side wheels of the vehicle to be aligned, and acalibration target 16 q. The two forward facing cameras 16 p, 16 r arenot rigidly connected to each other; that is, their relative positionand orientation can change. The calibration camera 16 s is rigidlyattached to the forward facing “Left” camera 16 g, and views thecalibration target 16 q that is rigidly attached to the forward facing“Right” camera 16 r. The calibration camera 16 s is used to measure therelative orientation of the forward facing cameras 16 p, 16 r in aconventional manner, as explained, e.g., in U.S. Pat. No. 6,968,282,incorporated by reference herein.

Camera pods 16 g and 16 h are supported by reversible arms 16 n.Reversible arms 16 n are attached to moveable carriage 17 b by removablepins 17 c (FIG. 19). Carriage 17 b can move the arms 16 n and camerapods 16 g, 16 h to follow the up or down motion of an alignment rack onwhich the vehicle to be aligned is parked. The arms 16 n can be foldedto minimize the width of the console 16 a to facilitate moving thealigner. The arms 16 n are shown in their folded position in FIG. 21.

In certain embodiments, all the major components of the disclosedaligner, including the two camera pods 16 g, 16 h, the display device,the printer 16 j, and any other I/O device such as a keyboard and mouse,communicate with each other wirelessly. In such embodiments, aconventional wireless communication “hub” which controls the wirelesscommunication is located at one of the components; e.g., one or both ofthe camera pods 16 g, 16 h. Also, the aligner can be connected to anexternal network (e.g., via the wireless communication hub) andcommunicate therewith, as explained in detail herein below.

An exemplary embodiment is shown in the system block diagram of FIG. 22.Camera pods 2201 and 2202 communicate via an ethernet cable 2203. Camerapod 2201 has a conventional wireless communication device (not shown)for communicating with a mini personal computer 2204, which may have amonitor 2205, a keyboard 2206, and a mouse 2207, as well as a wirelesscommunication device (not shown) for communicating with camera pod 2201.Camera pod 2202 also has a convention wireless communication device (notshown) for communicating with a shop wife unit 2208, which cancommunicate with the internet 2209, in a manner discussed herein below.

The cameras and camera circuit boards in camera pods 2201, 2202 facingthe wheel targets are identical, and both perform image processing. Incertain embodiments, alignment calculations are also performed by thecamera boards of pods 2201, 2202, and the results are communicated tothe mini PC 2204, which serves a user interface and displays thealignment results. Note that the terms “camera” and “camera board” areused interchangeably throughout this document, as those of skill in theart will appreciate. The calibration camera 2201 a mounted in the LHcamera pod 2201 that looks across at the calibration target 2202 amounted in the RH camera pod 2202 does no image processing and, as aresult has a simplified board. The image processing for the calibrationcamera 2201 a is done by the board for the LH camera 2201 b. There isalso a drive-on camera 2202 c mounted in the RH camera pod 2202 thatviews the vehicle as it drives onto the lift. It relies on the RH camera2202 b for image processing. In other embodiments, all image processingis performed by one camera board. Alternatively, the camera boards donot perform any image processing, and instead send raw images to themini PC 2204 for processing.

In other features of embodiments of the disclosed portable aligner, in a“standard” configuration the monitor 16 b of the display device, or thedisplay device itself, is attached to column 16 f by a bracket 17 a (seeFIG. 19). In an alternative configuration the monitor or display deviceitself is attached to the keyboard shelf 16 a via a conventional monitorstand. In this embodiment the bracket 17 a could be removed from thecolumn 16 f.

Base plate 16 e, column 16 f, moveable carriage 17 b, arms 16 n andcamera pods 16 g, 16 h comprise a self-contained measuring system thatcan be attached to mobile base 16 m, to the floor, or to a suitable wallor upright structure. Base plate 16 e contains a plurality of holes thatcan be used to attach base plate 16 e to mobile base 16 m. This sameplurality of holes could be used with floor anchors to attach themeasuring system to the floor. Column 16 f also has multiple brackets 17a, 17 d (see FIG. 19) that in a standard configuration mount the monitor16 b or display device and the keyboard shelf 16 a. In an alternativeconfiguration, these brackets 17 a, 17 d are used to attach themeasuring system to a wall or other suitable structure. To support thisalternative configuration, arms 16 n can be flipped via removable pins17 c.

Referring now to FIGS. 18 and 20, clamp hanger brackets 16 c areattached to clamp hanger structures 16 k using a plurality ofconventional fasteners. To accommodate end user preferences for clampstorage location, brackets 16 c are easily removable and can be attachedto another structure 18 c using conventional fasteners. To furtheraccommodate end user preferences for clamp storage location, the entireclamp hanger structure 16 k can be removed from the mobile base 16 m andattached to the floor using conventional floor anchors.

A wireless connection between the printer 16 j and the camera measuringsystem allows the printer to be installed in certain embodiments in thekeyboard shelf 16 a, or at any other user preferred location within thealignment shop, or within a nearby office or room.

A wireless connection between the display device, the wirelesscommunications hub, the user input devices such as keyboard and mouse,and/or the printer 16 j allows for the addition of a second fullydetached console in a further embodiment. This configuration allows amobile camera measuring system with a separately mobile user inputstation. In one embodiment shown in FIGS. 23a-c , a relatively small,more portable satellite console 2300 is provided that docks with themain console 2310. This satellite 2300 has a display device, aconventional power source (not shown) such as a lithium ion battery orequivalent, and optionally an input device such as a keyboard 2320 forthe user to enter data. It is self-sufficient on power for a period oftime, and communication to the main console 2310 is completely wireless.Such a portable console 2300 allows the user to undock the satellite andmove it to a location that allows visibility of the readings on thedisplay 16 b during an alignment. When not in use, the satellite 2300docks back to the main console 2310 and the two units nest together toform one unit. The satellite 2300 can dock with a portable or fixedstation. When docked with a portable station such as main console 2310,the two units function and move as one unit.

The docking functionality can be achieved by a number of well-knowntechniques. In one embodiment, mechanical latching/connecting device(s)secure the satellite console 2300 when docked. This mechanical deviceallows for simple and quick docking and undocking of the satelliteconsole 2300 to the main console 2310. Another way of implementing thedocking functionality is a magnetic mechanism 2330 to allow for simpleand quick docking and undocking. This mechanism 2330 can be anelectromagnet that is engaged/disengaged with the press of a button.Alternatively, it can be a permanent magnet that is engaged by simplyplacing the satellite console base 2300 a close enough to the mainconsole base 2310 a to engage the magnet 2330. To disengage thepermanent magnet 2330, a foot lever or other similar mechanism can beused to break the magnetic attraction between the two consoles 2300,2310.

When under a vehicle making alignment adjustments, it is difficult ifnot impossible to see the main display. A solution for alleviating theviewing disadvantages of having the main display at the front of thevehicle is to make the display height adjustable on the console, asshown in FIGS. 24a-25b . Adjusting the display to any height between thestandard viewing height to a height that is near the ground helpsviewing the display from underneath the vehicle. One way to provide thistype of adjustment is to mount the display device on a conventional-typepivoting arm(s) that adjusts to set upper and lower heights, as shown inFIGS. 24a-c . The arm(s) 2400 hold the display 2410 (which could be amonitor, a smart TV, etc.) above the console 2420 when in the upposition (FIGS. 24a-b ) and swing the display down in front of theconsole 2420 when in the down position (FIG. 24c ). Cable managementcould be maintained in the adjustable arm(s) 2400. Referring now toFIGS. 25a-b , another way to provide the desired adjustability is toslidably mount the display 2500 on rails 2510 that allow adjustment fromthe standard height, to near the ground, and between (FIGS. 25a-b ).Either of these mounting techniques could be implemented on a satelliteconsole such as console 2300 described herein above with reference toFIGS. 23 a-c, and/or a main console such as shown in FIGS. 25a-b . Cablemanagement could be maintained in the rail(s) 2510.

Aligner with Advanced Analytics

Embodiments of an aligner having advanced analytics will now bedescribed, which depends on high-frequency data collection andcontinuous or semi-continuous analysis of measurements. The disclosedadvanced analytics determine whether certain measurements are indicativeof operator error and/or equipment faults. The aligner uses the advancedanalytics to inform the operator of the error or fault and/or correctfor the error or fault. This functionality is enabled by the aligner'svery fast measurement process which; for example, calculates multipletarget poses for the vehicle wheels as they are moved, using optimizediterative algorithms to estimate the position and orientation of thereference targets. See, e.g., U.S. Pat. Nos. 5,535,522; 7,855,783;6,871,408; and 7,069,660; and U.S. Patent Application Publication2016/0195388, hereby incorporated by reference herein.

Aligner Architecture

FIG. 16 is a schematic top plan view of certain elements of acomputer-aided, 3D motor vehicle wheel alignment system (“aligner”),similar to that disclosed in U.S. Pat. No. 6,968,282 discussed hereinabove. The aligner of FIG. 16 can be used to implement the disclosedtechniques. In particular, the aligner of FIG. 16 comprises a leftcamera pod 2 and a right camera pod 4 that are used to align wheels of amotor vehicle. The terms “left” and “right” are used for convenience,and are not intended to require a particular element to be located in aparticular location or relationship with respect to another element.

Arrow 30 of FIG. 16 schematically represents a motor vehicle undergoingalignment. The vehicle includes left and right front wheels 22L, 22R andleft and right rear wheels 24L, 24R. An alignment target 80 a, 80 b, 80c, 80 d is secured to each of the wheels 22L, 24L, 22R, 24R,respectively. Each alignment target generally comprises a plate 82 onwhich target information is imprinted and a clamping mechanism 88 forsecuring the target to a wheel. A left camera pod 2 comprises leftalignment camera 10L. Left alignment camera 10L faces the vehicle andviews the left side targets 80 a, 80 b along axis 42. Right camera pod 4comprises a right camera 10R that faces the vehicle and views the rightside targets 80 c, 80 d along axis 44. Left camera pod 2 also includes acalibration camera 20 mounted perpendicularly to camera 10L. Calibrationcamera 20 views a calibration target 16 attached to right camera pod 4,to determine the positions of alignment cameras 10L, 10R relative toeach other.

The disclosed aligner further comprises a data processor (not shown),such as a conventional personal computer (PC), having software withinstructions to cause the data processor to perform the calculationsdescribed herein electronically.

The method and apparatus described herein is also applicable for usewith a hybrid aligner system similar to that described in U.S. Pat. No.7,313,869, which is hereby incorporated by reference in its entirety,and its continuation patents. FIG. 17 shows a schematic representationof an exemplary hybrid aligner system useable with the presentlydisclosed method and apparatus, including a pair of passive targets 21and 23 mounted on respective wheels 22 and 24 of the vehicle, which arefront steering wheels in this first example. A pair of active sensingheads 25 and 27 are adapted for mounting in association with otherrespective wheels 26 and 28 of the vehicle, in this case the rearwheels. Each active sensing head includes a camera 29 or 31 forproducing 2D image data, which is expected to include an image of one ofthe targets 21, 23 when the various heads are mounted to the respectivewheels of the vehicle. The system also uses two conventional (1D) anglesensors 33 and 35 to measure the relative angles of the active sensingheads 25 and 27 in the toe plane, and a pair of tilt sensors 37, 39 tomeasure tilt, typically camber and pitch, of heads 25, 27.

Fast Measurement Process

A key technology enabling the disclosed aligner and the functionalitydescribed herein is a very fast measurement process. For thisinnovation, the core measurements of interest are the poses (positionsand orientations) of targets that are rigidly mounted to the vehiclewheels. Performing a fast measurement process thus equates to performingmeasurements of target pose very rapidly. In imaging aligners, computingpose rapidly involves performing optimized image processing and applyingoptimized iterative algorithms to estimate the position and orientationof the reference targets. The high speed measurement process providesfor many updates and checks to be performed during the course of a wheelturning processes which may only take several seconds. To measure thepose of wheel mounted targets from individual cameras, such cameras 10Land 10R of FIG. 16, or cameras 16 p, 16 r of FIG. 18, it is essential tohave calibrated cameras. Calibrated cameras are ones which have hadtheir internal geometry (focal length, camera center point, lensdistortion) characterized by a camera calibration process.

Examples of well-known camera calibration processes are the Heikkilamethod; Zhang's method; the Faugeras-Luong method; the Hartley-Zissermanmethod; and the Triggs method. To compute the 3D pose of a target from asingle 2D camera it is further required to have knowledge of thegeometry of the target that is being observed. With knowledge of thetarget geometry and knowledge of the internal camera geometry, it ispossible to compute the 3D pose of that target based on a single 2Dimage. An example of a commercially available “fast” camera usable toimplement the disclosed aligners is the VC Z series camera availablefrom Vision Components GMBH of Ettlingen, Germany.

The process of computing target pose is conventional, and starts withacquiring an image of the wheel mounted target. This image is thenprocessed to identify image feature points that correspond to referencefiducials in the target. These reference image points are thenassociated with reference fiducial points in the target. Finally, aniterative pose estimation process is performed. Pose estimation answersthe question, “Where does a target need to be located, and how must itbe oriented, to produce the pattern of image feature points that Imeasured?”

In the disclosed aligners, the entire measurement process (imageacquisition, image processing, image feature point to target fiducialpoint correspondence, and pose estimation) is performed repeatedly in aloop for all targets used until target pose information is no longerneeded for the alignment process. To acquire measurements very rapidly(for example, greater than 20 poses per second) it is necessary toperform all four steps rapidly. The data processing steps must beimplemented with highly efficient algorithms and they must also beimplemented on processors with architectures that are optimized for theimage processing and numerical linear algebra steps used in the dataprocessing algorithms. Examples of well-known processors witharchitectures optimized for image processing and numerical linearalgebra include DSPs (Digital Signal Processors); GPUs (GraphicsProcessing Units); FPGAs (Field Programmable Gate Arrays); and ASICs(Application Specific Integrated Circuits). Examples of highly efficientdata processing algorithms include Gaussian filtering; gradient descentoptimization; Sobel edge detection; Canny edge detection; SURF featurepoint detection; and optical flow point tracking. An example of acommercially available processor with architecture optimized for theapplication of this disclosure is Model TDA2×ADAS DSP/ARM System-on-Chipprocessor, available from Texas Instruments of Dallas, Tex.

In certain embodiments, the data collection procedures and advancedanalytics described herein below with reference to FIGS. 1-15 areperformed using “intelligent cameras” that are fast and perform all thedata processing for the aligner, including serving the user interface,preprocessing image data, and calculating alignment parameters. Moreparticularly, the intelligent cameras (e.g., the camera pods 16 g, 16 hshown in FIG. 18) acquire images and preprocess the image data.Preprocessing generally includes manipulating image data to prepare itfor use in calculating alignment parameters, such as alignment angles.Examples of well-known preprocessing operations include backgroundsubtraction, gradient calculation, derivation of positional data, anddata compression for reduced bandwidth. Preprocessing of image data isdescribed in detail, for example, in U.S. Pat. No. 7,069,660.Preprocessed image data from both cameras is used to calculate alignmentangles, which are served to a display device. In other embodiments, onlyone of the two cameras performs the processing and calculating ofalignment parameters.

The data collection procedures and advanced analytics described hereinbelow with reference to FIGS. 1-15 are incorporated into embodimentssuch as the portable aligner shown in FIGS. 18-25 b. In suchembodiments, the cameras of camera pods 16 g and 16 h are intelligentcameras as just described. In still further embodiments, the advancedanalytics of FIGS. 1-15 are incorporated into a hybrid aligner system asdescribed herein above and in U.S. Pat. No. 7,313,869, herebyincorporated by reference in its entirety, and its continuation patentsincluding U.S. Pat. No. 7,313,869, also incorporated by reference in itsentirety.

Measurement, Detection, Correction, and Warning of Problems DuringRunout Compensation

To calculate the alignment of the vehicle, it is well-known that thealigner must locate the spindle of each wheel. To locate the spindle,the aligner typically measures the pose of the target attached to thewheel in at least two places before and after the wheel is rotated.Wheel spindle calculations are well-known, and exemplary algorithmsdescribed for such calculations are described in U.S. Pat. Nos.5,535,522 and 6,148,528. To locate the spindle accurately, the vehiclemust roll around a single axis. Any variation from a single axis rollwill cause errors. This process is commonly referred to as runoutcompensation or rolling runout compensation. Using fast processingcameras as discussed herein above, the disclosed aligner can takediscrete measurements at many places during the rolling procedure. Withthese measurements, the disclosed aligner can take many axiscalculations, as well as other calculations looking for potential errorsand solutions. Some of these errors might be a bump in the path of thevehicle's roll, a slide of the skid plates, or multiple axes ofrotation; e.g., an inadvertent turning of the steering wheel, or a wheelwobble due to bad bearings. See, e.g., U.S. Patent ApplicationPublication 2016/0195388.

There are other useful well-known calculations that can be made duringthe rolling process; for example, the center of the wheel can becalculated by looking at the path and pose of the targets through therolling process. This can be then used to establish the vehiclereference plane, or vehicle dimensions.

These advantageous fast measurements are not restricted to the cameraslooking at the wheels; the calibration camera (such as camera 20 of FIG.16 or camera 16 s of FIG. 18) can measure the calibration of the alignervery frequently and can be used to detect unwanted motion of the camerasduring the rolling procedure. This motion will cause errors in thecameras' pose measurements of the targets and ultimately create errorsin the spindle and alignment measurements. It is therefore desirable todetect camera motion, and not use the measurements obtained during thetime the camera(s) moved.

In typical imaging wheel alignment systems, one acquires images ofvehicle wheels with clamps that are rigidly mounted to each individualwheel rim, each clamp carrying a target that is viewed by one of thecameras. Examples of such clamp/target assemblies are target/clamp 80 a,88 of FIG. 16 and target/clamp 16 d of FIG. 18. In the course ofperforming a wheel alignment, it is standard procedure to perform runoutcompensation after attaching the clamps/targets by rolling the vehiclewheels in a back and forward manner. When doing so, it is assumed thatthe reference target rotates only about the axis of rotation of itsassociated wheel. It is also assumed that the wheel center travels in astraight line. In the real world, often with crooked alignment lifts andvarious obstacles laying on the lift surface, this ideal motion is notachieved. If these assumptions are not true they can degrade theaccuracy of alignment angle measurements.

The present disclosure addresses this problem by rapidly acquiring andrecording measurements of wheel-mounted target pose (i.e., targetposition and orientation) throughout the wheel rolling process andanalyzing these measurements to check the wheel alignment assumptions.

The process in which data is collected by the disclosed aligner isdepicted in FIG. 1. The vehicle is rolled continuously without a pause,and a number of poses of each wheel target is acquired; for example,about twenty (20) different poses if the vehicle is rolled quickly. Incertain embodiments, the image data captured by the cameras is usable tocalculate a minimum plurality of poses of the target; such as at leastone pose for every five degrees of wheel and target rotation. Thecompensation data acquisition process continues until each wheel rollsthrough an elapsed angle of rotation; for example, 35 degrees ofrotation. More particularly, at step 100 the vehicle is rolled, and atstep 110 a pose of the target on a wheel is acquired. The pose ischecked at step 120 against previously-stored poses for that wheel, andif it is sufficiently different, it is stored at step 130. At step 140it is determined whether the wheel has rolled enough (e.g., 35 degrees).If not, another pose is acquired for that wheel (step 110) and theprocess repeats. When the wheel has rolled enough, data collection stopsat step 150. When it has been determined all four wheels have beenrotated enough (step 160), the procedure is stopped at step 170.

Once the compensation data acquisition process completes, the poseslogged for all four wheels are then passed into through a series ofdiagnostics checks in a “Compensation Analytics Engine” shown in FIG. 2.Problems with the alignment lift itself can be detected and in somecases compensated for without requiring that the user performs thecompensation roll again. Some exemplary diagnostics checks areschematically described in FIG. 2.

One set of inputs for the Compensation Analytics Engine of FIG. 2 arethe stored poses acquired by the process of FIG. 1 (step 202). Anotherset of inputs for the Compensation Analytics Engine depicted in FIG. 2are the target spindle translation (TST) vectors for each of the fourwheels (step 200). These well-known calibrations can be performedoffline, or they can be computed from data collected during thecompensation roll in conjunction with outside information of the targetcenter point distance to the wheel rim along the wheel axis normaldirection. Those of skill in the art will understand that the TSTvectors, when anchored at the wheel mounted target centers, define thewheel center points. More specifically, a TST vector is the translationray from the target coordinate system to the center of the wheel rim. Asa ray, the origin of the TST is anchored to the origin of the targetcoordinate system, and the direction of the TST ray is fixed withrespect to the target coordinate system. The TST ray thus transforms intandem with changes to the pose of its associated target. The reason fordefining TST in such an invariant manner is to allow wheel center pointsto be recomputed as wheel positions change, upon measuring target posesin the new wheel position (i.e. as the vehicle steering wheel is turnedor as adjustments are made to individual camber and toe angles).

TST vectors can be computed using a number of well-known techniques. Onetechnique employs a “3D coordinate measurement machine,” wherein thewheel target comprises suitably positioned reference fiducial markers.These points are measured and then used to define the origin point andcoordinate axes of the target coordinate system. An additional set of 3Dcoordinates are obtained from tips of the three or four symmetricalwheel clamp “claws” of the clamp that holds the wheel target on thewheel. In a conventional self centering clamp, such as clamp assembly 16d shown in FIG. 18, the center of these “claw” points corresponds totheir center point, which also corresponds to the center of a wheel rimwhen the clamp is mounted to a wheel. The translation from the target tothe center of the clamp “claws” is thus measured in the coordinatemeasurement machine's coordinate system, and then transformed in aconventional manner to the target coordinate system defined from thereference fiducial markers on the target face.

As is clear from FIG. 2, there are multiple independent analytics checkswhich can be performed using the logged target poses and the TST vectorsas inputs. Each analytic test (step 204, 214, 224, 234) is essentially adata integrity check that checks an assumption of the compensationprocess. The basic format of each check follows a similar pattern. Foreach test, the large quantity of data collected is used to check for aproblem in the compensation step (steps 206, 216, 226, 236). If aproblem is encountered, the aligner attempts to correct for the problem(steps 208, 218, 228, 238). The user is alerted if a problem cannot becorrected (steps 210, 220, 230, 240); for example, by displaying theproblem on a display device (step 248). If all problems are successfullycorrected and/or no problem(s) detected (steps 212, 222, 232, 242), atstep 244 the alignment proceeds to the next alignment step (step 246).The analytics checks do not need to be performed in a predefinedsequence. They can be run independently of each other.

Surface Flatness Checks

One of the assumptions used when performing compensation is that thesurface over which the vehicle rolls is flat. In cluttered real worldscenarios, this may not necessarily be the case. As those of skill inthe art will appreciate, the alignment lift can possess largediscontinuities in its surface. Using the large quantity of datacollected during the compensation roll in the disclosed aligner, thesesurface continuities can be detected and in some cases corrected for. Anembodiment for performing the surface flatness checks at steps 204-212of FIG. 2 as part of the compensation procedure is illustrated ingreater detail in FIG. 3. The process of FIG. 3 is performed for allfour vehicle wheels.

The basic principle in the surface flatness detection algorithm is toemploy the fact that the center of a wheel rolling on a flat surfacemoves in a straight line. In this embodiment, the center of the wheel iscomputed by applying the TST (Target Spindle Translation) vector to eachtarget pose that was saved as part of the data acquisition process inFIG. 1 (step 300). The best fit line through the set of all computedwheel center points is then computed (step 302) as well as fit errorsfor individual data points (step 304). Deviations from a straight lineindicate error. If the RMS (root mean squared) deviation from the bestfit line is sufficiently low (step 306); i.e., lower than apredetermined threshold, then the lift surface is declared to be flat(step 308) and the process proceeds to the next analytics check.

A more complicated situation arises when the best fit line through thelift surface does not have a sufficiently low RMS fit error at step 306.In this case, the iterative outlier removal process is started (thebottom box inside of FIG. 3). In this process, the worst measurement isremoved (step 310), and the best fit line is recomputed without thisworst measurement (step 312), along with the fit errors for individualdata points (step 314). “Worst” here is the wheel center point with thelargest deviation from the best fit line. If the RMS error from the newbest fit line is sufficiently low (step 316) then the process terminatessuccessfully (step 318). If the RMS error at step 316 is still largerthan the tolerance then the worst remaining measurement is excluded, thebest fit line is recomputed, and the RMS error check is performedagainst the predetermined tolerance (steps 310-316).

This process of steps 310-316 iterates until either the best fit lineachieves a sufficiently low RMS error or until an excessive number ofdata points are removed, which is checked at step 320. The bare minimumnumber of 3D points required to uniquely specify a 3D line is two. Usingonly two points is inadmissible for this test, however, as two pointswill always form a perfect best fit line with no error whatsoever(defeating the purpose of this test). In practice, the minimum number ofmeasurements required is at least 6.

If outlier measurements are removed and the RMS best fit line error issufficiently low, then only those poses corresponding to the inlierwheel center point measurements are used to compute the wheel axis. Inthis case, the inconsistent rolling surface is corrected for and theprocess progresses to the next check.

If too many outlier measurements are removed, then the non-uniformrolling surface cannot be compensated for. The surface flatness checkfails and the user is alerted at step 322 that the observed surfacenon-uniformity couldn't be corrected. In such a scenario, the runoutcompensation process cannot be completed successfully (step 324).

There are various ways in which surface flatness checks could beimplemented. In the above-described embodiment, reference was made tothe RMS error of a best fit line fitted through the 3D wheel centerpoints. One could use additional optimization metrics other than the RMSerror. For example, one could use changes in the slope of the best fitline as the criteria to minimize other than the RMS best fit error.

Plate Slipping Detection

Wheel alignments are performed by the disclosed aligner on aconventional alignment lift, a specialized piece of equipment withmovable plates under the vehicle wheels (“turn plates” for the frontwheels and “slip plates” for the rear wheels). The plates can be set bythe technician in either a locked or an unlocked state. When locked, theplates do not translate when forces are applied to the surface. Whenunlocked, the plates translate freely when a force is applied. Thisallows for vehicle wheels to translate and rotate without inducingadditional stress in the vehicle suspension. During certain wheelalignment procedures such as runout compensation, however, it isnecessary for the plates to be locked and to not translate freely whenthe vehicle rolls over them. Free translation of a wheel means that itis free to translate without a proportional rotation about the wheelaxis. Free translation of wheels without corresponding rotation cancause errors in runout compensation.

The process for detecting plate slipping at steps 214-222 of FIG. 2 aspart of the compensation procedure is illustrated in greater detail inFIG. 4. Once the runout compensation data acquisition process of FIG. 1is complete, the measured target poses and TST calibration vectors areused as inputs to the plate slipping check (steps 200, 202).

As the vehicle rolls, for each wheel there must be an elapsed angle ofrotation that is proportional to the linear travel distance. The lineartravel distance is the distance translated by the center of each wheel.Formula 1 below shows the ideal proportional relationship between wheelcenter translation and elapsed angle of wheel rotation (in radians).This proportional relationship is used to perform a linear least squaresbest fit of wheel translation distance vs. angle of rotation. Errors ofpredicted vs. measured rotation are then used to detect and in somecases correct for observed instances of translation without rotation.d _(trans)∝θ_(rot)

Formula 1: Ideal Wheel Travel Distance and Elapsed Angle of Rotation

FIG. 5 shows two plots. The dotted plot shows a roll of a typical radiuswheel (12.4″) rolling with the ideal proportional relationship betweenwheel translation and rotation. The plot of x's shows a wheel roll ofthe same radius but with a slip plate unlocked and the wheelexperiencing translation without rotation at the end of the vehiclemotion.

As is schematically depicted in FIG. 5, the error metric for this testis the deviation from the best fit line of wheel linear translationdistance vs. elapsed angle of rotation. For the above comparison of aplate slipping vs. non-slipping scenario, we see the plot of best fitline error vs. rotation angle.

As shown in FIG. 6, instances of translation without rotation arereadily detected by examining errors in the best fit line. The largestbest fit line errors in the plate slipping scenario are significantlylarger than errors in the non-slipping scenario. By iteratively removingthe points where slipping occurs, the best fit line can be significantlyimproved.

Referring now to FIG. 4, the center of each wheel is computed byapplying the TST (Target Spindle Translation) vector to each target posethat was saved as part of the data acquisition process in FIG. 1 (step400). The translation and rotation from the initial point is thencomputed for all poses (step 402). The best fit line of elapsedtranslation versus elapsed rotation is then computed (step 404).Deviations from a straight line indicate error. If RMS error of the bestfit line as computed in FIG. 4 is sufficiently low when using all storedpose measurements i.e., lower than a predetermined threshold (typically<0.025″) at step 406, then the plate slipping check passes (step 408)and the process proceeds to the next step.

If the RMS error is not below this threshold then an instance oftranslation without rotation has been detected. The iterative outlierremoval process then runs. At each iteration, the worst measurement(with the largest absolute error in the best fit line) is removed (step410). Translations and rotations are recomputed (taking care to adjustinput poses for the omitted measurement) and the translation vs.rotation best fit line is recomputed (step 412) along with fit errorsfor the remaining data points (step 414). If the RMS error issufficiently low after omitting this worst point (step 416) then theprocess completes (steps 418, 408) and runout compensation proceeds tothe next step. This process of omitting the worst remaining point ofsteps 410-416 iterates until either a sufficiently good best fit lineRMS error is achieved or until too many points have been removed (thisis checked at step 420), at which time the user is alerted at step 422that the observed plate slipping problem couldn't be corrected, and theplate slipping check fails (step 424).

In the above-described embodiment of plate slippingdetection/correction, the criteria for detecting/correcting plateslipping is a proportionality check between wheel center pointtranslation vs. wheel rotation. With time-tagged pose data, in furtherembodiments the elapsed angle of rotation is interpolated to allmeasurement times on all wheels. With angle of rotation and time,angular velocity can be computed. If any time during the rollback, onewheel shows a much lower angle of rotation than the other wheels underobservation, then this information could be used to detect plateslipping.

Wheel Axis Precession Detection

The principle quantities of interest measured during the compensationroll are the wheel axes. The wheel axes are the (virtual or real)spindles about which each wheel rolls. It is assumed during thecompensation process that all measured rotation occurs about wheel axes.This might not be true, however. If a wheel does not closely approximatea circle during a roll then there will not be a unique wheel axis ofrotation. For example, if a tire is somewhat flat or if a rim is bent,the wheel may exhibit wobble as it rolls.

When a wheel possesses a significant amount of wobble, the true wheelaxis of rotation cannot be measured directly from changes in theorientation of the wheel mounted targets. Rather, the measured axes ofrotation will precess about an axis. This “axis of precession” is thetrue wheel axis of rotation. This phenomena is illustrated in FIG. 7,which depicts measured wheel axes 700 for a wheel with a large amount ofwobble (5 degrees). Ten measured wheel axes (dashed lines) 700 orbitabout their axis of precession (dark solid line) 710 as the wheelrotates through an elapsed angle of rotation of 45 degrees. Anembodiment for performing the wheel axis precession checks at steps224-232 of FIG. 2 as part of the compensation procedure will now beillustrated in greater detail.

The process for computing the axis of precession (i.e., the true wheelaxis) from individual measured axes of rotation is depicted in the flowchart of FIG. 8. To compute the axis of precession, the tips of thewheel axes vectors are used as input to a best fit plane computation.Standard best fit plane algorithms are used to compute the best fitplane through the input wheel axes vector tips. The basic idea is tocompute the true wheel axis (i.e. the axis of precession 710) from themeasured wheel axes of rotation 700. The wheel wobble angle is thencomputed between the axis of precession 710 and each individual wheelaxis of rotation 700.

Accordingly, as shown in FIG. 8, after retrieving the stored poses forthe vehicle wheels at step 800, wheel axis precession is detected bycalculating the wheel axis of rotation vectors of one of the vehiclewheels for each of the captured target poses of that wheel in aconventional manner (step 810), calculating an axis of precession basedon the calculated wheel axes of rotation (step 820), calculating a wheelwobble angle between the axis of precession and each of the calculatedwheel axes of rotation along with a standard deviation of the calculatedwobble angles (step 830), and determining whether the standard deviationof the wobble angles is below a predetermined error threshold (step840).

When the standard deviation of the wobble angles is below the errorthreshold, the wheel axis precession is compensated for by assigning theaxis of precession to be the true wheel axis for a following alignmentstep (step 850). When the standard deviation of the wobble angles isabove the error threshold, the user is alerted that the system cannotcorrect for the wheel axis precession (step 860).

The algorithm in which axis of precession is computed is described inFIG. 9. The process for computing the true wheel axis of individual axesof rotation is iterative. Individual axes of rotation may be moreerroneous than others and may thus degrade the accuracy of the best fitplane computation. For this reason, it is essential to remove individualwheel axes of rotation that are significantly more erroneous than theothers.

Accordingly, computing the axis of precession comprises inputting theset of all measured wheel axes of rotation found in step 810 (step 900),and computing a best fit plane through the tips of the calculated wheelaxis vectors (step 910). The root mean square (RMS) planarity error isthen computed at step 920, and it is determined whether a deviation fromthe best fit plane is below a predetermined RMS error threshold at step930.

When the deviation from the best fit plane is above the RMS errorthreshold, the wheel axis precession is computed by removing the wheelaxis of rotation vector with the largest deviation from the best fitplane (step 950), recomputing the best fit plane (step 910), recomputingthe RMS planarity error (step 920), determining whether the deviation isbelow the RMS error threshold (step 930), and repeating steps 950, 910,and 920 until the RMS deviation from the best fit plane is below theerror threshold or until less than a predetermined minimum number ofdata points remain (this is checked at step 960). If less than theminimum number of axes remain, the user is alerted that the systemcannot find a valid axis of precession, and therefore cannot correct forwheel axis precession (step 970). On the other hand, if the deviation isfound to be below the RMS error threshold at step 960, and more than theminimum number of data points remain, the normal of the best fit planeis assigned to be the axis of precession at step 940.

One alternative way in which wheel axis of rotation precession could bedetected is by stopping the vehicle at multiple points during thecompensation roll and collecting data at each of these stoppedpositions. Axes of rotation could be computed for poses collected ateach stopped position and the axis of precession could then be computedfrom these axes of rotation in an analogous manner to thepreviously-disclosed embodiment.

Camera Instability Detection

When rolling the vehicle during runout compensation, it is assumed thatthe measurement cameras are static. If the measurement cameras arebumped or otherwise moved during the data acquisition of FIG. 1,measurement errors will occur. It is thus crucial to detect for cameramotion that occurs during the compensation data collection. It isstraightforward to detect camera motion when it is known that wheelmounted targets are stationary. Detecting camera motion is moredifficult when targets are not stationary in scenarios such as runoutcompensation. An embodiment for performing the camera instability checksat steps 234-242 of FIG. 2 as part of the compensation procedure willnow be illustrated in greater detail.

In this disclosed embodiment, there are two forward facing cameras thatare not rigidly connected; that is, their relative position andorientation can change. For example, such a camera arrangement is shownin FIG. 18 as cameras 16 p, 16 r attached to foldable arms 16 n. A third“calibration” camera 16 s is used to measure the relative orientation ofthe forward facing cameras 16 p, 16 r (see, e.g., U.S. Pat. No.6,968,282). The calibration camera 16 s is rigidly attached to a forwardfacing camera 16 r. The calibration camera 16 s views a calibrationtarget 16 q that is rigidly attached to forward facing camera 16 p. Insuch a system, changes in the relative pose between the calibrationtarget 16 q and the calibration camera 16 s constitute changes in therelative pose between the forward facing cameras 16 p, 16 r. Thus,motion of the camera system can be detected if the relative pose betweenthe forward facing cameras changes.

An embodiment for performing the camera instability checks at steps234-242 of FIG. 2 as part of the compensation procedure will now beillustrated in greater detail with reference to FIG. 10. It assumes thehardware configuration of the embodiment described above; i.e., acalibration camera measures the pose of a calibration targetconcurrently with forward facing cameras measuring wheel mounted targetposes. As each new calibration target pose is received, it is comparedto the initial calibration target pose that was acquired at thebeginning of the compensation procedure. If the new measurement oftarget pose is significantly different from the stored initialcalibration target pose, then the relative camera pose is unstable. Inother words, either one or all forward facing cameras have been movedwith respect to the others. As a result, the compensation procedure isstopped and the user is alerted that the assumption of stable cameras isnot valid.

Accordingly, detecting instability of one or more of the forward facingcameras comprises starting the compensation procedure (step 1000) bymeasuring an initial calibration target pose in that camera's coordinatesystem (step 1005). As the vehicle rolls (step 1010), a new calibrationtarget pose is acquired (step 1015). At steps 1020-1030, if the newtarget pose does not correspond to the calibration target (i.e., itcorresponds to a wheel mounted target), the pose is used as input to thelarger runout compensation process, and the amount of wheel rotation ischecked to determine whether all four vehicle wheels have rotated anadequate amount to complete the compensation procedure.

At step 1035, the initial calibration target pose is compared with thesucceeding one of the calibration target poses, to determine whether itdeviates from the initial calibration target pose more than a thresholdamount (step 1040). The compensation procedure is stopped (step 1045)and the user is alerted that the cameras are unstable when one of thesucceeding calibration target poses deviates from the initialcalibration target pose more than the threshold amount (step 1050).Otherwise, the process is repeated until the wheels have rotated anadequate amount.

Differences in the calibration target pose are computed for bothrotation and translation. As a representative threshold, new calibrationtarget poses must be within a translation of 0.15″ and a rotation of0.15° from the initial stored calibration target pose.

Through this process, data is acquired for performing runoutcompensation and for performing the various analytics checks describedelsewhere. The camera instability process is run online during thecompensation data acquisition process. If at any time the calibrationtarget pose is measured to be too different from the reference targetpose, the user is alerted that the camera instability is too large andthe procedure is stopped.

There are various other ways in which detection of camera instabilityduring compensation could be implemented. One could use additionalsensors. For example, if there were accelerometers rigidly attached toeach forward facing camera, one could monitor the accelerations todetermine when camera pods change their pose. Additionally, camerainstability detection can be implemented by securing a fixed referencetarget (or a series of such targets) to a rigid structure while stillremaining in the field of view of forward facing cameras. For example,the targets could be secured to a wall or to a post on the floor. Thepose of the reference targets could be measured throughout thecompensation process. If the fixed reference target pose changes, asmeasured by any of the forward facing cameras, then it can be readilydetermined if the static camera assumption made during compensation isvalid or not.

Detection of Suspension Changes During Alignment

The aligner measures the pose of the targets attached to the wheels andcalculates the alignment of the vehicle after every pose measurement.When the user is making adjustments to the vehicle or performingprocedures on the vehicle, he could make mistakes, do proceduresincorrectly, or skip procedures all together, resulting in an inaccuratealignment. By taking fast measurements, the disclosed aligner can lookfor and detect when the operator makes one of these mistakes and informhim earlier, to prevent unwanted delay and give him quick feedback tohelp him learn and prevent future error repetitions.

A recognized source of measurement error when performing vehicle wheelalignments is stress changes induced in the vehicle suspension. If avehicle suspension is stressed beyond typical rest conditions, thestress tends to gradually release as the vehicle is moved/adjustedduring the course of an alignment procedure. This release of thesuspension stress can significantly change wheel alignment angles. Thisgradual changing of wheel alignment angles can be particularly harmfulin procedures like runout compensation. If a vehicle suspensionde-stresses during runout compensation, the runout compensation could besignificantly incorrect and as a result vehicle wheel alignment anglescould be adjusted to incorrect values. This could in turn causepremature tire degradation and/or result in sub-optimal driving comfort.

There are several ways vehicle suspensions could become prematurelystressed in the course of performing alignment angle measurements. It iscommon for alignment technicians to raise vehicles up on alignment liftsto inspect the vehicle undercarriage prior to performing anymeasurements or adjustments. If the vehicle is lowered to the alignmentlift surface and the slip plates are locked, the vehicle suspension canbecome stressed as the weight of the vehicle becomes supported by thesuspension and the suspension is not free to adjust to its restingposition. Another common way in which vehicle suspensions can becomestressed is if the vehicle experiences a sharp turn immediately prior todriving onto the alignment lift. The sharp turn induces stress into thesuspension and the very short straight line transit distance(essentially the length of the alignment lift) does not provide enoughopportunity for the suspension to release.

Whatever the cause, the gradual release of suspension stress during thecourse of an alignment is a problem that if not detected will negativelyimpact the alignment process. There are various ways in which thesuspension stress change can be detected. One embodiment is depicted inFIG. 1 and FIG. 11. In this embodiment, the suspension stress check isperformed as part of the runout compensation procedure. It can be partof the Compensation Analytics Engine of FIG. 2.

The data collection loop is depicted in FIG. 1. As discussed hereinabove, in this procedure, the vehicle is rolled through a rotation anglebetween start and stop positions. As the vehicle rolls between the startand stop positions, pose (position and orientation) measurements of thefour wheel-mounted targets are acquired at semi-regular time or rotationintervals. The time stamps at which the saved pose data was acquired canalso be collected. This is done in case image acquisition for all fourwheels is not time-synchronized.

Once the data collection has finished for all four wheels (i.e., onceeach wheel has experienced enough elapsed rotation), a suspension stressdetection algorithm can be run on the acquired target pose measurements.The computations and decision logic for checking if this suspensionchange occurred is depicted in FIG. 11. The basic idea of the algorithmis to compute toe and camber angles through the full range of data thatwas collected in the process of FIG. 1. Then, for all the toe and camberangles at all time or rotation steps, checks are performed looking forlarge deviations in these alignment angles. Toe and camber anglesshouldn't change as a vehicle is rolled back and forward, and thisinvariance is used to perform in-situ checks of alignment angleconsistency. If alignment angles show a significant change during therollback (e.g., about 0.1 degrees of camber/toe change) then thisindicates the suspension of the vehicle is releasing stress during thevehicle roll. To summarize, in this embodiment detecting stress in thesuspension of the vehicle comprises computing at least one wheelalignment parameter for each of the vehicle wheels for each of thecaptured target poses of that wheel, calculating changes in the wheelalignment parameter for successive captured target poses, anddetermining whether the changes are below a predetermined changethreshold.

As shown in FIG. 11, the stored poses for the vehicle wheels areretrieved at step 1100, and are used to compute the wheel axes in aconventional manner (step 1105). The TST vectors are then retrieved atstep 1110, and the wheel center points for all poses are computed. Atsteps 1115 to 1130, toe and camber angles are derived at all dataacquisition times by well-known techniques, and at step 1135 changes inthe toe and camber angles are computed at all data acquisition times.Then, at step 1140, the camber and toe changes for each wheel arecompared to a threshold value as discussed herein above, and if thestress change is larger than the threshold, the user is alerted at step1145. If the stress change is less than the threshold, the systemproceeds to the next alignment procedure at step 1150.

In FIG. 11, reference is made to TST (Target Spindle Translation) atstep 1110. As discussed herein above, this is a calibration thatquantifies the translation ray from the center of the reference targetto the center of the wheel rim. It is used to find the location of wheelrim centers. These wheel rim centers are then used to compute the VCS(Vehicle Coordinate System), which is the frame of reference for wheelalignment angles in the preferred embodiment.

Pose measurements for each wheel might not be acquired in a timesynchronized manner. For such a scenario, when computing the VCS fromthe wheel rim center points it is essential to interpolate the wheelcenter points to all pose measurement times. These interpolated wheelcenter points are then used to compute the VCS at all measurement times.See steps 1115 and 1120.

A key consideration of this disclosure is that the suspension stresscheck is performed “behind the scenes,” such that the end user isn'tburdened with additional tasks or with unnecessary information. The useris only given instructions/notifications in the event suspension stressis detected during the normal course of events performed as part of analignment process. This decision logic is depicted in the lowerright-hand rectangle inside of FIG. 11.

A key enabling technology for this steady state monitoring is a veryfast measurement process. In FIG. 11, the core measurement of interestis the pose (position and orientation) of a target that is rigidlymounted to a vehicle wheel. Performing a fast measurement process thusequates to performing measurements of target pose very rapidly. Inimaging aligners, computing pose rapidly involves performing optimizedimage processing and applying optimized iterative algorithms to estimatethe position and orientation of the reference targets, as discussedherein above.

The high speed measurement process provides for many updates andmultiple checks (approximately 20 poses collected per wheel during afast vehicle roll) to be performed during the course of measurementprocesses which may only take several seconds. The very fast measurementprocess allows the steady state suspension stress monitoring to be runin the background, not providing any updates to the user unless aproblem is detected. This provides for a very user-friendly method toidentify measurement problems.

Once a change is discovered, it would be helpful if instead of awarning, the aligner compensated for the change and continued on withthe alignment with the corrected readings. To do this, in certainembodiments the measurement change of specific alignment readings of thevehicle is plotted on a graph or table or a formula for a givenprocedure. Using these empirically derived referenceplots/table/formula, the aligner matches where the current vehicle undertest is in the settling process and predicts where it will be oncecompletely settled. Thus, instead of alerting the user to a suspensionstress change in step 1145, the system could compare the change to anempirical reference value or use the change in an empirical formula topredict a value for the alignment parameter when the suspension issettled, and use the predicted value in the subsequent alignment step.As an example, if a vehicle is under stress at the beginning of the rollback procedure, one way to remove the stresses is to roll it backwardsand forwards 3 or 4 times. From empirical testing it is noted that afterthe first roll back only 50% of the stress is relieved. Therefore, whenthe current vehicle under test has a change of say 0.1 degree after thefirst roll, it is predicted that if it were rolled backward and forward3 more times to its fully settled state, it would change 0.2 degree.This full settlement value could be used to display what that actualreading would be if the vehicle were fully settled.

There are various alternative techniques in which suspension stresschanges can be identified. The above-described embodiment of FIGS. 1 and11 uses changes in camber and toe angles as the metric for identifyingsuspension stress changes. However, it is possible, for example, toinstead compute and monitor the wheel axes of rotation during the courseof the runout compensation procedure. For a pure translation of thevehicle chassis (as is experienced during runout compensation), thewheel axes should not change direction. If they do gradually changedirection, this is an indication of suspension stress release.

In the embodiment of FIGS. 1 and 11, the suspension stress check isperformed at multiple steps throughout the compensation roll withoutstopping. It is, however, possible to pause at multiple steps during thecompensation roll process and compute the various suspension stressquantities at each step. This requires more cooperation from the enduser and is thus less user friendly, but such a series of stops wouldprovide the same suspension stress checks as in the preferredembodiment.

Repeated reference is made to a Vehicle Coordinate System in FIG. 11.The disclosed technique does not however require a VCS. Any frame ofreference suitable for expressing wheel alignment angles could be usedin lieu of the VCS as defined above. The core principle of thedisclosure is to search for changes in alignment angles, however suchmay be computed. Likewise, reference is made in FIG. 11 to TSTcalibrations which are used to compute Vehicle Coordinate Systems. Theuse of TST calibrations to compute such Vehicle Coordinate Systems islikewise not the only possible way to implement this feature of thedisclosed aligner.

The disclosed suspension stress change monitoring is performed duringthe runout compensation process. This is done out of convenience as theuser performs this process regardless of whether or not suspensionstress is monitored. However, the disclosed suspension stress check doesnot have to be performed as part of the compensation procedure. It couldbe performed as a stand-alone procedure (for example, for alignmentsystems that do not require runout compensation) or it could beperformed in any other alignment procedure where the suspension stressmetrics should remain invariant.

Thrust Angle Checking Procedures

The vehicle thrust direction is the direction along which vehicle moves.In passenger vehicles, thrust is defined by the relative toe angles ofthe rear wheels. Thus, when performing measurements on the front wheelsthe thrust direction shouldn't change. By continuously monitoring thevehicle thrust angle during the course of various front wheelmeasurements, the disclosed aligner can detect problems in the alignmentlift without tasking the end user with any additional measurement steps.This functionality is enabled by the aligner's very fast measurementprocess, which calculates multiple target poses for the vehicle wheelsas they move.

FIG. 12 depicts the process in which a thrust angle is computedinitially for a particular measurement procedure (runout compensation).The basic idea is that thrust angle is measured at the start of ameasurement process in a conventional manner. To compute thrust angle inan invariant Vehicle Coordinate System (VCS), one must first compute thetoe angle in that same VCS. To compute toe angle in this invariantcoordinate system, one must perform runout compensation (step 1200) anddetermine and store the wheel axes for each individual wheel (step1210). The next steps are to compute the VCS from individualmeasurements of pose for the wheel mounted targets (step 1220) andproject the wheel axes to the VCS (step 1230). The toe angles are thencomputed for all four wheels (step 1240), and the thrust angle iscomputed from the rear toe angles (step 1250). This initial thrust angleis stored as a reference angle at step 1260.

Equation 2 shows the well-known formula for computing thrust angle instep 1250. Essentially, thrust angle is the average difference betweenthe right rear and left rear toe angles.

$\begin{matrix}{{{Computation}\mspace{14mu}{of}\mspace{14mu}{Thrust}\mspace{14mu}{Angle}}{{thrust} = \frac{{{Right}\mspace{14mu}{Rear}\mspace{14mu}{Toe}\mspace{14mu}{Angle}} - {{Left}\mspace{14mu}{Rear}\mspace{14mu}{Toe}\mspace{14mu}{Angle}}}{2}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$FIG. 13 illustrates the steady-state thrust angle monitoring process ofthis embodiment. When a new pose for a wheel target is acquired at step1300, as during the compensation procedure of FIG. 1, the VCS isrecomputed using the new pose measurement (step 1310), the wheel axescomputed at step 1210 are projected to the updated VCS (step 1320). Toeangles are recomputed at step 1330, and the thrust angle is recomputedat step 1340. The recomputed thrust angle is compared to the initialreference thrust angle in step 1350, if it deviates more than apredetermined threshold amount, the user is alerted to a problem at step1360. In certain embodiments, the thrust angle check of FIG. 13 isperformed as part of the runout compensation procedure. It can be partof the Compensation Analytics Engine of FIG. 2.

The steady state monitoring process of FIG. 13 runs when performingvarious measurements where the vehicle moves but where thrust angleshould remain invariant despite the vehicle motion, such as duringcaster measurement, runout compensation, and when performing adjustmentsto front wheel alignment angles.

Where and how might this steady state monitoring of thrust angle beuseful? For example, when performing caster swing the front wheels areturned left to right or right to left. During this process, the slipplates on the alignment lift are unlocked and the vehicle is free totranslate along the two dimensional surface of the alignment lift. Iffor example the left rear slip plate is not free to move but the platesfor the other three wheels are free to translate, there will be stressinduced in the left rear wheel. This stress can manifest itself bytwisting the wheel so as to change the toe angle on the left rear wheelwithout significantly changing right rear toe angle. As per Equation 2above, such a series of events will result in a change in the thrustangle. In such a scenario the rapid steady state updates of thrust angleof the disclosed aligner (e.g. 20 updates per second) can be used toalert the user that a problem has been detected which could result in adeficient alignment for the vehicle under consideration.

A key enabling technology for this steady state monitoring is a veryfast measurement process. In FIG. 13, the core measurement of interestis the pose (position and orientation) of a target that is rigidlymounted to a vehicle wheel. Performing a fast measurement process thusequates to performing measurements of target pose very rapidly. Inimaging aligners, computing pose rapidly involves performing optimizedimage processing and applying optimized iterative algorithms to estimatethe position and orientation of the reference targets.

The high speed measurement process provides for many updates (e.g.,approximately 20 poses collected per wheel during a fast vehicle roll)and checks to be performed during the course of measurement processeswhich may only take several seconds. The very fast measurement processallows the steady state thrust angle monitoring to be run in thebackground, not providing any updates to the user unless a problem isdetected. This provides for a very user-friendly method to identifydeficiencies in support equipment.

In the embodiment of FIGS. 12-13 above, thrust angle is computed atfinely spaced time intervals during the course of a measurement process.Thrust angle is the preferred reference quantity because it is aproperty of the vehicle that is invariant to many changes in thevehicle, and because it can be used as a check in multiple commonalignment measurement processes. There are, however, alternative metricsone could use in a manner similar to thrust angle. For example, onecould use individual rear toe angles, individual rear camber angles,total rear camber angles, and other measurements that are invariant tomany common vehicle motions.

In the embodiment of FIGS. 12-13, a Vehicle Coordinate System is used asthe invariant coordinate system in which the reference measurement iscomputed. Alternative invariant coordinate systems to a VCS couldhowever be used to similar effect. For example, a stationary referencetarget coordinate system off the vehicle lift could be used as the basisof all measurements.

In addition, it is not required to perform the aforementioned thrustangle checks at very frequent time intervals. A slower measurementprocedure wherein the end user must repeatedly pause the system so thatthrust angle (or other invariant metric) can be recomputed and checkedagainst the reference steady state value could be performed to similareffect as the above-described embodiment. It has the disadvantage ofrequiring more time and more effort of the end user.

Non-Static Runout Compensation

The disclosed aligner allows a technician to perform a runout procedurequickly by pushing and/or pulling the vehicle so its wheels roll throughan angle of rotation continuously without pausing at turnaround points.This functionality is enabled by the aligner's very fast measurementprocess, which calculates multiple target poses for the vehicle wheelsas the vehicle is rolled (e.g., at least one pose for every five degreesof rotation).

A faster, more user-friendly measurement of runout compensation isprovided via a procedure hereinafter referred to as “rapid runout.” Inrapid runout, the user pushes the vehicle to roll through an angle ofrotation. As the vehicle starts rolling, measurements are collected andlogged for post-processing until the elapsed angle of rotation isexperienced. Each “measurement” consists of the pose (position andorientation) of a target that is rigidly attached to one of the fourvehicle wheels. Throughout the roll, measurements are acquired steadilyfor all vehicle wheels at semi-regular intervals.

If measurements show a significant rotation from the previous storedmeasurements for their associated wheel then they get stored forpost-processing. The non-static compensation data acquisition process isdepicted in FIG. 1, and is the same in all relevant respects as theprocedure described herein above.

The data acquisition process loops until sufficient elapsed rotation(e.g., 35 degrees) has been measured for all vehicle wheels. The salientpoint is that data is collected as the vehicle rolls without requiringany pauses or decisions from the end user. Data collection stopsautomatically as well. The process is seamless.

The pose measurements that are collected and monitored consist oftranslation and orientation components of the observation targets in areference coordinate system. Pose is computed from image acquisitions ofthe reference targets on individual wheels.

Data collection is not required to start and stop in a time-synchronizedmanner for all four wheels. Once a given wheel has been measured toexhibit sufficient rotation, data collection for that wheel stops. Datacollection continues for other wheels until they have been measured toexperience enough elapsed rotation. Once sufficient rotation has beenmeasured for all wheels, the collected target pose data is passed to anon-static compensation algorithm. The end result of the algorithm isthe wheel axis for each vehicle wheel.

This process is depicted in FIG. 14. At step 1400, all stored poses fora rotated wheel are retrieved. For all combinations of target posepairs, (steps 1410, 1420), the wheel axis and angle of rotation betweenthem is computed (step 1430). If the angle of rotation between them islarger than a predetermined threshold amount at step 1440, the wheelaxis is stored at step 1450. When all combinations of target pose pairshave been iterated, the average wheel axis of all the stored wheel axesis computed at step 1460.

The wheel axis is used later in the wheel alignment process to computetoe and camber alignment angles. It is thus of paramount importance tomeasure this quantity as accurately as possible. The basic principle incomputing the wheel axis, as shown in FIG. 14, is to loop over allcombinations of poses (compared two at a time) and between each pair ofposes compute the axis of rotation and angle of rotation about thataxis. If the elapsed angle of rotation is sufficiently large (e.g., 15degrees) then store that wheel axis and use it later to compute the meanwheel axis. If the angle of rotation between a pair of poses isn't largeenough then the accuracy of the wheel axis direction will suffer andthat axis of rotation shouldn't be used later to compute the mean wheelaxis.

The axis of rotation can be computed at step 1430 using a number ofstandard well-known techniques. In one embodiment, the 3×3 rotationmatrix that rotates the target coordinate axes from one pose measurementto the next is computed. Then, an eigenvector/eigenvalues decompositionis performed on this 3×3 rotation matrix. The eigenvector correspondingto the principal eigenvalue is then assigned to be the axis about whichthe target rotates, i.e. the axis of rotation.

A key enabling technology for this steady state monitoring is a veryfast measurement process, as described herein above. The high speedmeasurement process provides for many updates and checks to be performedduring the course of a vehicle roll which typically only takes severalseconds. The very fast measurement process allows for data redundancywhich can enable a more accurate measurement of runout compensation.Most importantly, the user does not need to pause and wait while targetposes are being acquired.

There are alternative embodiments by which non-static compensation couldbe achieved. The specific rolling procedure in which data collectionoccurs is not material to the present disclosure. For example, in someapplications it is preferable to roll the vehicle to the back of thealignment lift and then return it to the front without pausing. Inothers (for example, in so-called “audit” configurations) it may bepreferable to perform one roll without a corresponding return to theinitial position. In other scenarios it may be preferable to performseveral consecutive shorter rolls. In all scenarios the data acquisitionprocess of FIG. 1 and the wheel axis computation algorithm of FIG. 14can be carried out for every rolling motion.

In the embodiment of FIGS. 1 and 14, the entire car rolls and all fourwheels rotate in tandem (though not necessarily by the same angle). Inother embodiments, all 4 wheels do not rotate concurrently. For example,the non-static compensation procedure as described above could becarried out while a vehicle is elevated and wheels are rotated inisolation without any corresponding translational motion of the vehiclechassis. In another embodiment, so-called “two wheel runout” can beperformed where only two of the wheels (typically the front wheels) haverunout compensation performed on them. The same data collection and dataprocessing algorithms are used in these special cases.

In the embodiment of FIGS. 1 and 14, repeated reference is made to poses(positions and orientations) of reference targets that are rigidlyattached to wheels. However, the use of fixed reference targets that arerigidly attached to wheels is not strictly required. One could measurethe position of a cluster of 3D points rigidly attached to a wheelduring the compensation roll. The rigid body transformation of thiscluster of points could then be used to compute the wheel axis ofrotation at discrete measurement times during the compensation roll.These 3D points that are tracked during the compensation roll could betextured feature points present on the wheel, or they could be referencefiducial points attached to the wheel rim as part of the compensationmeasurement process.

The end result of the disclosed non-static runout compensation processfor each wheel is a single averaged wheel axis vector. However, thecomputation of a mean wheel axis is not strictly required. For example,one could perform additional statistical analysis to select the mosttypical “median” wheel axis vector. Additional methods could be used tofind the resultant wheel axis vector from the set of stored wheel axes.The disclosure does not depend on a statistical combination ofindividual wheel axes (mean, median, or other). One could simply takethe wheel axis to be the axis of rotation from the largest observedangle of rotation in the set of stored poses for that wheel. The key isthat wheel axes are computed without requiring the user to pause andhold at any point during the data acquisition process.

Non-Static Caster Swing

The disclosed aligner allows a technician to perform a caster swingquickly by turning the vehicle wheels continuously without pausing atturnaround points. This functionality is enabled by the aligner's veryfast measurement process, which calculates multiple target poses for thevehicle wheels as they are turned.

A principle quantity of interest in vehicle wheel alignment is casterangle. Caster is defined as the back/forward inclination angle of thesteering axes for the front vehicle wheels. To measure this inclinationangle of the steering axis it is necessary to make the vehicle frontwheels exhibit a rotation about their steering axes. In other words, onemust turn the front wheels left and right an appreciable amount toenable an accurate measurement of the steering axes. In typical wheelalignment systems, this process of turning left and right requires apause at each of the various turnaround points. This pause addsunnecessary delay to the caster angle measurement process.

This unnecessary pause is eliminated in a process hereinafter called“non-static caster”. This measurement process is depicted in FIG. 15. Atstep 1500, the initial poses of all four wheel-mounted targets areacquired (i.e., while the wheels are not being turned), and the initialVCS is computed in a conventional manner (step 1505). The caster swingprocedure starts at step 1510 as the user turns the steerable wheels ofthe vehicle, typically the front wheels, capturing image data of thetarget as the wheel and target are continuously turned to the left andto the right of center without a pause (step 1515). The captured imagedata is usable to calculate a minimum plurality of poses of the target;e.g., at least one pose for every 5 degrees of turning of the wheels. Aseach target pose is acquired (step 1520), the VCS is updated (i.e.,recomputed) with the new pose data at step 1525. The system noteswhether the target pose is for a front wheel at step 1530, and if sowhether there is enough rotation about the steering axis from theprevious front wheel pose (step 1535), and if so whether the wheels havebeen turned less than a predetermined number of degrees (step 1540). Ifthe answer to any of these questions is “no,” the process iterates. Whenboth front wheels have turned an adequate number of degrees, the casterswing procedure is stopped at step 154, and the caster angles arecomputed using the final stored poses for all four wheels (step 1550) ina conventional manner.

A key enabling technology for this steady state monitoring is a veryfast measurement process, as described herein above, which equates toperforming measurements of target pose very rapidly. The high speedmeasurement process provides for many updates and checks to be performedduring the course of a wheel turning process which may only take severalseconds.

To compensate for the camera and processing not being fast enough, incertain alternative embodiments the aligner collects and stores imagesand only process every Nth image (for example, every 10th image). Thistechnique results in an effect similar to a non-static castermeasurement, in that the aligner can instruct the user to stop turningthe wheel as soon as it measures enough rotation about the steering axiswithout a pause, and then instruct him to turn the wheel back, all thewhile saving the images of this “burst mode” for future processing. Inthe background, when bandwidth is available the saved images areprocessed; however, there would be a delay after the caster swing iscomplete for all the images to be processed and the caster measured andultimately displayed to the technician. Burst mode has certaindisadvantages because (1) the user does not know as quickly whensufficient rotation has occurred; (2) the LED strobe isn't being updatedas regularly as when using “fast” processors so the strobe might be toodim or too bright, producing reduced quality measurements; (3) asignificant amount of fast data storage is required; and (4) there is apause after the swing is complete for the measured values to bedisplayed.

Alternative Embodiments

In the embodiment of FIGS. 1-15, repeated reference is made to poses(positions and orientations) of reference targets that are rigidlyattached to wheels. The disclosed techniques do not strictly require theuse of fixed reference targets that are rigidly attached to wheels. Onecould measure the position of a cluster of 3D points rigidly attached toa wheel during the compensation roll. The rigid body transformation ofthis cluster of points could then be used to compute the wheel axis ofrotation at discrete measurement times during the compensation roll.These 3D points that are tracked during the compensation roll could betextured feature points present on the wheel, or they could be referencefiducial points attached to the wheel rim as part of the compensationmeasurement process.

All of the above checks in the compensation analytics engine can beperformed in real-time, as the compensation rolling motion of thevehicle is performed. It is not required to gather a complete set oftarget poses along the full range of the compensation roll to performthe various integrity checks.

Fully Networked Aligner

When an aligner is in need of repair, a service technician is sent tothe site to work on the aligner. Sometimes the issue could be resolvedwithout a costly service call if the service technician could operatethe aligner remotely. Methods exist for taking control of an alignerremotely using special software and an internet connection, but they arelimited in their capabilities. See, e.g., U.S. Pat. Nos. 8,073,586 and8,452,484, which are hereby incorporated by reference in their entiretyherein.

There is a need for a remote display/interface for the technician onsite using the equipment, and a remote display/interface that servicepersonnel at a different location can use to control the aligner, all ona simple readily available device. A solution that easily does both ofthese in one simple architecture will now be disclosed. In thisembodiment, a wireless network is employed to physically disconnect theconstituent hardware components of the aligner to each other and to theinternet. This enables a wireless device such as a laptop, tablet,mobile phone, or any other device with a web browser to connect to thealigner to provide a remote display/interface for the aligner.

As shown in FIG. 26, the disclosed aligner can be wirelessly connectedto two networks: a first network connecting the aligner parts (e.g., thetwo camera housings and the display and a remote device) and the shopnetwork (e.g., the internet), and the two networks communicate with eachother.

In certain embodiments, a wireless network is employed to physicallydisconnect the constituent hardware components of the aligner. It isdesirable that the main processing component of the system whichcomputes the measurement angles remains fixed in front of the vehiclefor the duration of the alignment procedure. This component establishesa secure wireless access point that can be accessed by any number ofwireless devices. An application server runs software services thatsupport client connections via any standards-compliant web browser. Theuser can connect to the wireless access point and interact with thebrowser with a laptop, tablet, mobile phone, or any other device with aweb browser. It becomes unnecessary for the technician to physicallyinteract with the fixed measuring device during an alignment procedure.The technician is able to perform all the necessary steps to completethe vehicle alignment in the process of physically adjusting thesuspension. This provides both a time and cost savings over currentsolutions.

Additionally, the disclosed networked aligner can establish a clientconnection to an external WiFi access point, to provide internet accessto the software components running in the system. This is advantageousfor software/firmware version management, maintenance, and troubleshooting. Providing the ability to access the equipment over theinternet translates directly to more up-time. It becomes unnecessary fora service technician to visit the aligner in person to diagnose issuesor to install software updates. This saves the owner time because themachine is typically down waiting for the service technician, whichcould take hours or days.

CONCLUSION

The summary and abstract sections may set forth one or more but not allexemplary embodiments of the present invention as contemplated by theinventor(s), and thus, are not intended to limit the present inventionand the appended claims in any way.

Embodiments of the present invention have been described above with theaid of functional building blocks illustrating the implementation ofspecified functions and relationships thereof. The boundaries of thesefunctional building blocks have been arbitrarily defined herein for theconvenience of the description. Alternate boundaries can be defined solong as the specified functions and relationships thereof areappropriately performed.

The foregoing description of the specific embodiments will so fullyreveal the general nature of the invention that others can, by applyingknowledge within the skill of the art, readily modify and/or adapt forvarious applications such specific embodiments, without undueexperimentation, without departing from the general concept of thepresent invention. Therefore, such adaptations and modifications areintended to be within the meaning and range of equivalents of thedisclosed embodiments, based on the teaching and guidance presentedherein. It is to be understood that the phraseology or terminologyherein is for the purpose of description and not of limitation, suchthat the terminology or phraseology of the present specification is tobe interpreted by the skilled artisan in light of the teachings andguidance.

The breadth and scope of the present invention should not be limited byany of the above-described exemplary embodiments.

Exemplary embodiments of the present invention have been presented. Theinvention is not limited to these examples. These examples are presentedherein for purposes of illustration, and not limitation. Alternatives(including equivalents, extensions, variations, deviations, etc., ofthose described herein) will be apparent to persons skilled in therelevant art(s) based on the teachings contained herein. Suchalternatives fall within the scope and spirit of the invention.

What is claimed is:
 1. A vehicle wheel alignment system comprising: atleast one camera for viewing a respective set of a plurality ofthree-dimensional points on a respective wheel of the vehicle andcapturing image data of the set of points as the wheel is continuouslyrotated a number of degrees of rotation without a pause, wherein theimage data is used to calculate a plural minimum number of poses of thewheel as the wheel is continuously rotated the number of degrees ofrotation without a pause; and wherein the at least one camera comprisesa data processor for performing the steps of preprocessing the imagedata, and calculating an alignment parameter for the vehicle based onthe preprocessed image data.
 2. The system of claim 1, wherein the dataprocessor is for serving a user interface.
 3. The system of claim 1,wherein the data processor is for calculating a change in an alignmentparameter for the vehicle wheel based on the preprocessed image data,and for analyzing the change in the alignment parameter to detect anerror.
 4. The system of claim 3, where the detected error includes atleast one of (a) vehicle wheel axis precession; (b) surface flatnesserrors of a surface of an alignment rack on which the vehicle wheelsrotate; (c) plate slipping errors of a plate which comprises part of thesurface of the alignment rack; and (d) stress in a suspension of thevehicle beyond predetermined rest conditions.
 5. The system of claim 4,wherein the data processor is for attempting to correct or compensatefor detected problems (a)-(d).
 6. The system of claim 5, wherein thedata processor is for alerting a user of the system if the processorcannot correct the detected problems.
 7. The system of claim 1, whereinthe data processor is for: detecting, at least in part based on theimage data, at least one of (e) instability of the at least one camera;and (f) excessive vehicle thrust angle changes.
 8. The system of claim7, wherein the data processor is for attempting to correct or compensatefor detected problems (e)-(f).
 9. The system of claim 8, wherein thedata processor is for alerting a user of the system if the processorcannot correct the detected problems.
 10. The system of claim 1, whereincalculating the poses comprises (g) acquiring, as a first wheel isrotating, a first pose of the first wheel, (h) storing the first posewhen the first pose is different from previously stored poses of thefirst wheel to a predetermined degree, (i) repeating steps (g) and (h)when the first wheel has been rotated less than a minimum number ofdegrees of rotation; and (j) performing steps (g) through (i) for allthe wheels of the vehicle having a set of three-dimensional points. 11.The system of claim 1, wherein each of the minimum number of poses ofthe wheel is stored.
 12. The system of claim 1, wherein the minimumnumber of poses of the wheel comprises at least one pose for every fivedegrees of rotation captured by the at least one camera as the wheel iscontinuously rotated the number of degrees of rotation without a pause.13. The system of claim 1, wherein the set of three-dimensional pointsare rigidly attached to a respective wheel.
 14. The system of claim 1,wherein each of the three-dimensional points comprises textured featureson the wheel.
 15. A vehicle wheel alignment system comprising: at leastone camera for viewing a respective target disposed at a respectivewheel of the vehicle and capturing image data of the target as the wheeland target are continuously rotated a number of degrees of rotationwithout a pause, wherein the image data is used to calculate a minimumnumber of poses of the target as the wheel and target are continuouslyrotated the number of degrees of rotation without a pause; and whereinthe at least one camera comprises a data processor for performing thesteps of: preprocessing the image data; and calculating an alignmentparameter for the vehicle based on the preprocessed image data.
 16. Thesystem of claim 15, wherein the data processor of the at least onecamera is for serving a user interface.
 17. The system of claim 15,wherein the data processor is for calculating a change in an alignmentparameter for the vehicle wheel based on the preprocessed image data,and for analyzing the change in the alignment parameter to detect anerror.
 18. The system of claim 17, where the detected error includes atleast one of (a) vehicle wheel axis precession; (b) surface flatnesserrors of a surface of an alignment rack on which the vehicle wheels andtargets rotate; (c) plate slipping errors of a plate which comprisespart of the surface of the alignment rack; and (d) stress in asuspension of the vehicle beyond predetermined rest conditions.
 19. Thesystem of claim 18, wherein the data processor is for attempting tocorrect or compensate for detected problems (a)-(d).
 20. The system ofclaim 19, wherein the data processor is for alerting a user of thesystem if the processor cannot correct the detected problems.
 21. Thesystem of claim 15, wherein the data processor is for: detecting, atleast in part based on the image data, at least one of (e) instabilityof the at least one camera; and (f) excessive vehicle thrust anglechanges.
 22. The system of claim 21, wherein the data processor is forattempting to correct or compensate for detected problems (e)-(f). 23.The system of claim 22, wherein the data processor is for alerting auser of the system if the processor cannot correct the detectedproblems.
 24. The system of claim 15, wherein calculating the posescomprises (g) acquiring, as a first wheel is rotating, a first pose ofthe target at the first wheel, (h) storing the first pose when the firstpose is different from previously stored poses of the first wheel to apredetermined degree, (i) repeating steps (g) and (h) when the firstwheel has been rotated less than a minimum number of degrees ofrotation; and (j) performing steps (g) through (i) for all the wheels ofthe vehicle having a target.
 25. The system of claim 15, wherein each ofthe minimum number of poses of the target is stored.
 26. The system ofclaim 15, wherein the minimum number of poses of the target comprises atleast one pose for every five degrees of rotation captured by the atleast one camera as the wheel and target are continuously rotated thenumber of degrees of rotation without a pause.